1,234 research outputs found
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
Uncovering interactions in the frequency domain
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and
network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by
introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with
exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and
successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multielectrode
array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs
recorded from distributed sites in the right hemisphere of a macaque monkey
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation Analysis
In this paper, we propose a mixture of probabilistic partial canonical
correlation analysis (MPPCCA) that extracts the Causal Patterns from two
multivariate time series. Causal patterns refer to the signal patterns within
interactions of two elements having multiple types of mutually causal
relationships, rather than a mixture of simultaneous correlations or the
absence of presence of a causal relationship between the elements. In
multivariate statistics, partial canonical correlation analysis (PCCA)
evaluates the correlation between two multivariates after subtracting the
effect of the third multivariate. PCCA can calculate the Granger Causal- ity
Index (which tests whether a time-series can be predicted from an- other
time-series), but is not applicable to data containing multiple partial
canonical correlations. After introducing the MPPCCA, we propose an
expectation-maxmization (EM) algorithm that estimates the parameters and latent
variables of the MPPCCA. The MPPCCA is expected to ex- tract multiple partial
canonical correlations from data series without any supervised signals to split
the data as clusters. The method was then eval- uated in synthetic data
experiments. In the synthetic dataset, our method estimated the multiple
partial canonical correlations more accurately than the existing method. To
determine the types of patterns detectable by the method, experiments were also
conducted on real datasets. The method estimated the communication patterns In
motion-capture data. The MP- PCCA is applicable to various type of signals such
as brain signals, human communication and nonlinear complex multibody systems.Comment: DSAA2017 - The 4th IEEE International Conference on Data Science and
Advanced Analytic
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Information Flow in Computational Systems
We develop a theoretical framework for defining and identifying flows of
information in computational systems. Here, a computational system is assumed
to be a directed graph, with "clocked" nodes that send transmissions to each
other along the edges of the graph at discrete points in time. We are
interested in a definition that captures the dynamic flow of information about
a specific message, and which guarantees an unbroken "information path" between
appropriately defined inputs and outputs in the directed graph. Prior measures,
including those based on Granger Causality and Directed Information, fail to
provide clear assumptions and guarantees about when they correctly reflect
information flow about a message. We take a systematic approach---iterating
through candidate definitions and counterexamples---to arrive at a definition
for information flow that is based on conditional mutual information, and which
satisfies desirable properties, including the existence of information paths.
Finally, we describe how information flow might be detected in a noiseless
setting, and provide an algorithm to identify information paths on the
time-unrolled graph of a computational system.Comment: Significantly revised version which was accepted for publication at
the IEEE Transactions on Information Theor
Beyond element-wise interactions: identifying complex interactions in biological processes
Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations.
Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction.
Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem
Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise
We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
- …